Candlestick Plotting with Python Libraries

Introduction

In this post, we will be looking at creating candlestick charts, which are integral to traders’ daily routines, using Python libraries such as Mplfinance, Plotly and Bokeh, and look at customizing the plots, change themes and tweak some colors.
As this blog post is written on Quarto, it enables viewers to interact with the output plots. You’ll be able to pan and zoom with the charts created with plotly and bokeh!
So, now lets head on to fetch our data first.

Getting Data

We will make use of the yfinance library. Let’s fetch the Open-High-Low-Close and Volume data for Apple (AAPL) for the past 5 months.

Our data looks like this:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import yfinance as yf
import pandas as pd


data = yf.download("AAPL", period="5mo",auto_adjust=True)
data.head()

[*********************100%%**********************]  1 of 1 completed

	Open	High	Low	Close	Volume
Date
2023-11-24	190.626752	190.656713	189.008822	189.727905	24048300
2023-11-27	189.677965	190.427010	188.659261	189.548126	40552600
2023-11-28	189.538144	190.836490	189.158623	190.157349	38415400
2023-11-29	190.656711	191.845197	188.729178	189.128662	43014200
2023-11-30	189.598056	190.077455	187.950165	189.707916	48794400

Plots

Now we will head straight into plotting. We will start with mplfinance, a special module for finance charts of matplotlib.

mplfinance

Creating candlestick charts with mplfinance is a simple one line code:

import mplfinance as mpf

mpf.plot(data, type="candle", volume=True, style="yahoo",
         figsize=(6,3),title="AAPL Candlestick with Mplfinance",
        ylabel="Price [USD]")

This plot uses the “yahoo” style. We can check the available style using the command mpf.available_styles(). Now let us look at a few other available styles below:

mc_list = ["binance","blueskies","starsandstripes","brasil"]

fig = mpf.figure(figsize=(6,6))
ax_dict = {}

for i,st in enumerate(mc_list):
    ax_dict[i]=fig.add_subplot(2,2,i+1, style=st)
    mpf.plot(data[:50], type ="candle", ax=ax_dict[i], axtitle=(st),xrotation=0)

And now we will tweak and play with the candle colors and background. First we will set the base mplstyle to dark_background (for reference, check here). Then we will set marketcolors for the chart:

mc = mpf.make_marketcolors(up="g",down="r",inherit=True)
s = mpf.make_mpf_style(base_mpl_style="dark_background",marketcolors=mc)
mpf.plot(data, type="candle", volume=True, style=s,
         figsize=(6,3),title="Customised Dark theme plot on mplfinance",
        ylabel="Price [USD]")

For looking further stye changes using mplfinance, one might check their example notebook

We will move ahead and try hands on some interactive plotting libraries.

Plotly

Plotly is perhaps the most used interactive visualization library in python. It offers a great deal of customization. With Plotly, we will make two types of charts, OHLC and Volume plotted together in first one, separate in the next.

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Candlestick(x=data.index, open=data["Open"],
                              high=data["High"], low=data["Low"],
                             close=data["Close"], name = "OHLC"), secondary_y=False)

fig.add_trace(go.Bar(x=data.index, y=data['Volume'], opacity=0.3,
                     name= "Volume"), secondary_y=True)

fig.update_layout(title="AAPL Candlestick with Plotly", width=900, height=400)
fig.update_yaxes(title="Price [USD]", secondary_y=False, showgrid=True)
fig.update_yaxes(title="Volume", secondary_y=True, showgrid=False)

fig.show()

In the next one, we hide the range slider for better viewing and separate the volume bar chart:

fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, row_width=[0.2, 0.7])

fig.add_trace(go.Candlestick(x=data.index, open=data["Open"],
                              high=data["High"], low=data["Low"],
                             close=data["Close"], name = "OHLC"), row=1, col=1)

fig.add_trace(go.Bar(x=data.index, y=data['Volume'], opacity=0.3,
                     name= "Volume"), row=2, col=1)

fig.update_layout(title="AAPL Candlestick with Plotly", width=900, height=500)

fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()

Plotly as well offers multiple themes and customizations, for now will use the dark theme and just change the colors of volume bars for the first plot.

fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Candlestick(x=data.index, open=data["Open"],
                              high=data["High"], low=data["Low"],
                             close=data["Close"], name = "OHLC"), secondary_y=False)

fig.add_trace(go.Bar(x=data.index, y=data['Volume'], opacity=0.3,
                     name= "Volume", marker_color="gray"), secondary_y=True)

fig.update_layout(title="AAPL Candlestick with Plotly", width=900, height=500, xaxis={"rangeslider":{"visible":False}}, template="plotly_dark")
fig.update_yaxes(title="Price [USD]", secondary_y=False, showgrid=True)
fig.update_yaxes(title="Volume", secondary_y=True, showgrid=False)

fig.show()

Bokeh plot

Quoting:

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

Bokeh additionally has great documentation and user guide. Now let’s look at a candlestick chart created using Bokeh:

from bokeh.layouts import column
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource, RangeTool, BoxAnnotation, HoverTool
from bokeh.resources import INLINE


output_notebook(resources=INLINE)

inc= data.Close > data.Open
dec = data.Open > data.Close
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width_set = pd.Timedelta("16H")


fig = figure(x_axis_type="datetime", tools=TOOLS, width=900, height=350,
             title="AAPL Candlestick with Bokeh")

fig.segment(data.index, data.High, data.index, data.Low, color="black")
fig.vbar(data.index[dec], width_set, data.Open[dec], data.Close[dec], color="red")
fig.vbar(data.index[inc], width_set, data.Open[inc], data.Close[inc], color="green")
fig.yaxis.axis_label="Price [USD]"

vol = figure(x_axis_type="datetime", tools=TOOLS, width=900, height=150, x_range=fig.x_range)
vol.vbar(data.index, width=width_set, top=data.Volume, fill_color="grey", alpha=0.5)
vol.yaxis.axis_label="Volume"

show(column(fig,vol))

Loading BokehJS ...

Great! We will now change to dark theme. Check out : bokeh themes.

from bokeh.plotting import curdoc
from bokeh.themes import Theme
output_notebook(resources=INLINE)

inc= data.Close > data.Open
dec = data.Open > data.Close
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
width_set = pd.Timedelta("16H")


fig = figure(x_axis_type="datetime", tools=TOOLS, width=900, height=400,
             title="AAPL Candlestick with Bokeh")

fig.segment(data.index, data.High, data.index, data.Low, color="white")
fig.vbar(data.index[dec], width_set, data.Open[dec], data.Close[dec], color="red")
fig.vbar(data.index[inc], width_set, data.Open[inc], data.Close[inc], color="green")
fig.yaxis.axis_label="Price [USD]"

doc = curdoc()
doc.theme = "dark_minimal"
doc.add_root(fig)
show(fig)

Loading BokehJS ...

That really looks good. If you are going through the code here, you can see how simple it would be to change the colors of bars. One can further add tooltips that will display the OHLC data on hovering over the candles.

One might further look at libraries like Altair, ggplot. One great library that I just found while writing this post is Holoviews

Have fun plotting!